Correlation
Correlation is a statistical measure that quantifies the degree of association or relationship between two variables. In other words, it helps us understand how two variables tend to move in relation to each other.
Correlation provides a straightforward way to examine the result. The correlation value falls within the range of [-1, 1]. Refer to the table below:
Correlation with Python
To calculate correlation, use the np.corrcoef() function from numpy, which requires two parameters: the data sequences for which correlation is to be computed. Here's an example:
123456789import pandas as pd import numpy as np df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/Stores.csv') # Calculating correlation corr = np.corrcoef(df['Store_Area'], df['Items_Available'])[0,1] print(corr)
Here, we extracted the value at index [0, 1], just like in the case of covariance. In the previous chapter, we obtained the value 74955.85, and interpreting the result of the covariation function can be challenging. However, in this case, we can conclude that the values are strongly related.
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Correlation is a statistical measure that quantifies the degree of association or relationship between two variables. In other words, it helps us understand how two variables tend to move in relation to each other.
Correlation provides a straightforward way to examine the result. The correlation value falls within the range of [-1, 1]. Refer to the table below:
Correlation with Python
To calculate correlation, use the np.corrcoef() function from numpy, which requires two parameters: the data sequences for which correlation is to be computed. Here's an example:
123456789import pandas as pd import numpy as np df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/Stores.csv') # Calculating correlation corr = np.corrcoef(df['Store_Area'], df['Items_Available'])[0,1] print(corr)
Here, we extracted the value at index [0, 1], just like in the case of covariance. In the previous chapter, we obtained the value 74955.85, and interpreting the result of the covariation function can be challenging. However, in this case, we can conclude that the values are strongly related.
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